Breath analysis of ammonia, volatile organic compounds and deuterated water vapor in chronic kidney disease and during dialysis

Bioanalysis ◽  
2014 ◽  
Vol 6 (6) ◽  
pp. 843-857 ◽  
Author(s):  
Simon J Davies ◽  
Patrik Španěl ◽  
David Smith
Keyword(s):  
Chronic Kidney Disease ◽  
Water Vapor ◽  
Volatile Organic Compounds ◽  
Kidney Disease ◽  
Organic Compounds ◽  
Breath Analysis ◽  
Deuterated Water ◽  
Volatile Organic

Insights into a “seesaw effect” between reducibility and hydrophobicity induced by cobalt doping: Influence on OMS-2 nanomaterials for catalytic degradation of carcinogenic benzene

2021 ◽  
Author(s):  
chunlan Ni ◽  
Jingtao Hou ◽  
Qian Zheng ◽  
Mengqing Wang ◽  
Lu Ren ◽  
...  
Keyword(s):  
Water Vapor ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Catalytic Degradation ◽  
Cobalt Doping ◽  
Volatile Organic

OMS-2 is one of the most promising catalytic nanomaterials for the elimination of volatile organic compounds. However, water poisoning resulting from water vapor inevitably leads to the deactivation of active...

scholarly journals Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons

Metabolites ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 317
Author(s):  
Michalis Koureas ◽  
Paraskevi Kirgou ◽  
Grigoris Amoutzias ◽  
Christos Hadjichristodoulou ◽  
Konstantinos Gourgoulianis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Ambient Air ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Healthy Controls ◽  
Volatile Organic

The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological computed tomography (CT) findings not diagnosed with LC, and 53 healthy controls. The concentrations of 19 volatile organic compounds (VOCs) were quantified in the exhaled breath of study participants by solid phase microextraction (SPME) of the VOCs and subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Kruskal–Wallis and Mann–Whitney tests were used to identify significant differences between subgroups. Machine learning methods were used to determine the discriminant power of the method. Several compounds were found to differ significantly between LC patients and healthy controls. Strong associations were identified for 2-propanol, 1-propanol, toluene, ethylbenzene, and styrene (p-values < 0.001–0.006). These associations remained significant when ambient air concentrations were subtracted from breath concentrations. VOC levels were found to be affected by ambient air concentrations and a few by smoking status. The random forest machine learning algorithm achieved a correct classification of patients of 88.5% (area under the curve—AUC 0.94). However, none of the methods used achieved adequate discrimination between LC patients and patients with abnormal computed tomography (CT) findings. Biomarker sets, consisting mainly of the exogenous monoaromatic compounds and 1- and 2- propanol, adequately discriminated LC patients from healthy controls. The breath concentrations of these compounds may reflect the alterations in patient’s physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis.

scholarly journals Stability and Selective Vapor Sensing of Structurally Colored Lepidopteran Wings Under Humid Conditions

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3258
Author(s):  
Gábor Piszter ◽  
Krisztián Kertész ◽  
Zsolt Bálint ◽  
László Péter Biró
Keyword(s):  
Water Vapor ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Color Change ◽  
Response Times ◽  
Principal Component ◽  
Time Dependent ◽  
Dependent Manner ◽  
Vapor Sensing ◽  
Volatile Organic

Biological photonic nanoarchitectures are capable of rapidly and chemically selectively sensing volatile organic compounds due to changing color when exposed to such vapors. Here, stability and the vapor sensing properties of butterfly and moth wings were investigated by optical spectroscopy in the presence of water vapor. It was shown that repeated 30 s vapor exposures over 50 min did not change the resulting optical response signal in a time-dependent manner, and after 5-min exposures the sensor preserved its initial properties. Time-dependent response signals were shown to be species-specific, and by using five test substances they were also shown to be substance-specific. The latter was also evaluated using principal component analysis, which showed that the time-dependent optical responses can be used for real-time analysis of the vapors. It was demonstrated that the capability to detect volatile organic compounds was preserved in the presence of water vapor: high-intensity color change signals with short response times were measured in 25% relative humidity, similar to the one-component case; therefore, our results can contribute to the development of biological photonic nanoarchitecture-based vapor detectors for real-world applications, like living and working environments.

scholarly journals Breath Analysis for Early Detection of Malignant Pleural Mesothelioma: Volatile Organic Compounds (VOCs) Determination and Possible Biochemical Pathways

Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1262 ◽  
Author(s):  
Alessia Di Gilio ◽  
Annamaria Catino ◽  
Angela Lombardi ◽  
Jolanda Palmisani ◽  
Laura Facchini ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Early Detection ◽  
Organic Compounds ◽  
Malignant Pleural Mesothelioma ◽  
Breath Analysis ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Pleural Mesothelioma ◽  
Healthy Controls ◽  
Volatile Organic

Malignant pleural mesothelioma (MPM) is a rare neoplasm, mainly caused by asbestos exposure, with a high mortality rate. The management of patients with MPM is controversial due to a long latency period between exposure and diagnosis and because of non-specific symptoms generally appearing at advanced stage of the disease. Breath analysis, aimed at the identification of diagnostic Volatile Organic Compounds (VOCs) pattern in exhaled breath, is believed to improve early detection of MPM. Therefore, in this study, breath samples from 14 MPM patients and 20 healthy controls (HC) were collected and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC/MS). Nonparametric test allowed to identify the most weighting variables to discriminate between MPM and HC breath samples and multivariate statistics were applied. Considering that MPM is an aggressive neoplasm leading to a late diagnosis and thus the recruitment of patients is very difficult, a promising data mining approach was developed and validated in order to discriminate between MPM patients and healthy controls, even if no large population data are available. Three different machine learning algorithms were applied to perform the classification task with a leave-one-out cross-validation approach, leading to remarkable results (Area Under Curve AUC = 93%). Ten VOCs, such as ketones, alkanes and methylate derivates, as well as hydrocarbons, were able to discriminate between MPM patients and healthy controls and for each compound which resulted diagnostic for MPM, the metabolic pathway was studied in order to identify the link between VOC and the neoplasm. Moreover, five breath samples from asymptomatic asbestos-exposed persons (AEx) were exploratively analyzed, processed and tested by the validated statistical method as blinded samples in order to evaluate the performance for the early recognition of patients affected by MPM among asbestos-exposed persons. Good agreement was found between the information obtained by gold-standard diagnostic methods such as computed tomography CT and model output.

A systematic review of breath analysis and detection of volatile organic compounds in COPD

2016 ◽  
Vol 10 (3) ◽  
pp. 034002 ◽  
Author(s):  
Anders Christiansen ◽  
Jesper Rømhild Davidsen ◽  
Ingrid Titlestad ◽  
Jørgen Vestbo ◽  
Jan Baumbach
Keyword(s):  
Systematic Review ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Breath Analysis ◽  
Volatile Organic
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